What is Cancer?

1 What is Cancer?

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1.1 Replication Crisis

https://en.wikipedia.org/wiki/Replication_crisis

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1.2 Replication Crisis Excel Version

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2 RStudio ile proje oluştur

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3 R Notebook

3.1 R Notebook dökümanı oluşturma

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3.2 R Notebook’tan html, pdf ve word oluşturma

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3.3 RNotebook vs RMarkdown

https://youtu.be/zNzZ1PfUDNk

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4 R Markdown

4.1 Hem kendi kodları hem de html kodları yazılabilir

https://rmarkdown.rstudio.com

What is R Markdown? from RStudio, Inc. on Vimeo.

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4.2 R Markdown: The Definitive Guide

https://bookdown.org/yihui/rmarkdown/

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4.3 R Markdown syntax

https://gist.github.com/MinhasKamal/7fdebb7c424d23149140

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4.4 Remedy Package

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4.4.1 Remedy

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4.5 R Markdown paket ve şablonları

https://bookdown.org/yihui/rmarkdown/document-templates.html

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4.6 Render Markdown via code

inside R

markdown::markdownToHTML('markdown_example.md', 
'markdown_example.html')

command line

R -e "markdown::markdownToHTML('markdown_example.md',
'markdown_example.html')"

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4.7 pandoc Rstudio integration

command line

export PATH=$PATH:/Applications/RStudio.app/Contents/MacOS/pandoc

R -e "rmarkdown::render('markdown_example.md')"

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5 RMarkdown chunk içinde R kodlarını çalıştırma

{r, results='asis'}
 iris %>%
  tibble::as_tibble() %>%
  details::details(summary = 'tibble')

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6 Metin arasında R kodlarını çalıştırma

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7 Chunk Options

7.1 Global Options

{r global_options, include=FALSE}
knitr::opts_chunk$set(fig.width = 12,
                      fig.height = 8,
                      fig.path = 'Figs/',
                      echo = FALSE,
                      warning = FALSE,
                      message = FALSE,
                      error = FALSE,
                      eval = TRUE,
                      tidy = TRUE,
                      comment = NA)

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7.2 Other Code Languages

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8 R Markdown kod örneği

{r}
data("cancer")
cancer
foreign::write.foreign(df = cancer,
                        datafile = "data/cancer.sav",
                        codefile = "data/cancer.spo",
                        package = "SPSS"
                        )

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9 R Markdown Paket Çağırma 📦

{r}
suppressPackageStartupMessages(library("tidyverse"))
suppressPackageStartupMessages(library("survival"))

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9.1 Sık kullandığım paketler 📦

{tidyverse} {tidylog}

{lubridate} {janitor}

{readxl} {foreign}

{summarytools} {ggstatsplot} {tangram} {finalfit} {psycho} {jmv}

{survival} {survminer}

{report} {kableExtra}

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10 R Markdown Veri Yükleme SPSS

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11 R Markdown Veri Yükleme Excel

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12 Veri Görüntüleme

{r}
View(mydata)
glimpse(mydata)

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13 Veri Düzenleme

{r}
mydata <- janitor::clean_names(mydata)

{r}
mydata$sontarih <- janitor::excel_numeric_to_date(
  as.numeric(mydata$olum_tarihi)
  )

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14 Recode

{r}
mydata$Outcome <- "Dead"
mydata$Outcome[mydata$olum_tarihi == "yok"] <- "Alive"

{r}
## Recoding mydata$cinsiyet into mydata$Cinsiyet
mydata$Cinsiyet <- recode(mydata$cinsiyet,
               "K" = "Kadin",
               "E" = "Erkek")
mydata$Cinsiyet <- factor(mydata$Cinsiyet)

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15 Recode regular expression

{r recode TNM stage}
#pT2N0Mx -> 2
mydata$Tstage <- stringr::str_match(
  mydata$patolojik_evre, 
  paste('(.+)', "N", sep=''))[,2]
)

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16 Recode regular expression case_when

{r recode TNM2}
mydata <- mydata %>% 
    mutate(
        T_stage = case_when(
            grepl(pattern = "T1", x = .$Tstage) == TRUE ~ "T1",
            grepl(pattern = "T2", x = .$Tstage) == TRUE ~ "T2",
            grepl(pattern = "T3", x = .$Tstage) == TRUE ~ "T3",
            grepl(pattern = "T4", x = .$Tstage) == TRUE ~ "T4",
            TRUE ~ "Tx"
        )
    )

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17 Recode regular expression case_when

{r}
mydata <- mydata %>% 
    mutate(
TumorPDL1gr1 = case_when(
        t_pdl1 < 1 ~ "kucuk1",
        t_pdl1 >= 1 ~ "buyukesit1"
    )
    )

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18 R Markdown Tanımlayıcı İstatistikler

{r}
library(summarytools)
view(dfSummary(colon_s))

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A beginner kit for #rstats The Landscape of R Packages for Automated Exploratory Data Analysis https://journal.r-project.org/archive/2019/RJ-2019-033/

@article{RJ-2019-033, author = {Mateusz Staniak and Przemysław Biecek}, title = {{The Landscape of R Packages for Automated Exploratory Data Analysis}}, year = {2019}, journal = {{The R Journal}}, doi = {10.32614/RJ-2019-033}, url = {https://journal.r-project.org/archive/2019/RJ-2019-033/index.html} }

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18.1 Table One

{r, results='asis'}
# cat(names(mydata), sep = " + \n")
library(arsenal)
tab1 <- tableby(~ Cinsiyet + 
Yas + 
TumorYerlesimi
                ,
                data = mydata)
summary(tab1)

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18.2 The Grammar of Tables

tangram: The Grammar of Tables

A grammar of tables

Grammar of Tables?

Easily generate information-rich, publication-quality tables from R

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18.3 Kategorik Veriler

{r}
mydata %>% 
  janitor::tabyl(Categorical) %>%
  adorn_pct_formatting(rounding = 'half up',
                       digits = 1) %>%
  knitr::kable()

{r crosstable}
mydata %>%
    summary_factorlist(dependent = dependent, 
                       explanatory = explanatory,
                       total_col = TRUE,
                       p = TRUE,
                       add_dependent_label = TRUE) -> table
knitr::kable(table, row.names = FALSE, align = c('l', 'l', 'r', 'r', 'r'))

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18.4 Kategorik Veriler için Grafikler

{r ggstatplot, layout='l-page'}
mydata %>% 
    ggstatsplot::ggbarstats(data = .,
                            main = Categorical_variable,
                            condition =  dependent_variable
                            )

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18.5 Continious Variables

{r}
mydata %>% 
jmv::descriptives(
    data = .,
    vars = c(yas),
    hist = TRUE,
    dens = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sd = TRUE,
    variance = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE)

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19 R Markdown örneği Çapraz Tablolar

{r crosstable}
library(finalfit)
mydata %>%
    summary_factorlist(dependent = dependent, 
                       explanatory = explanatory,
                       column = TRUE,
                       total_col = TRUE,
                       p = TRUE,
                       add_dependent_label = TRUE,
                       na_include=FALSE
                       # catTest = catTestfisher
                       ) -> table
knitr::kable(table,
             row.names = FALSE,
             align = c('l', 'l', 'r', 'r', 'r'))

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20 R Markdown örneği Sağkalım

• Drawing Survival Curves Using ggplot2
  https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html

20.1 Sağkalım için veriyi düzenleme

{r define survival time}
mydata$int <- lubridate::interval(
  lubridate::ymd(mydata$CerrahiTarih),
  lubridate::ymd(mydata$SonTarih)
  )
mydata$OverallTime <- lubridate::time_length(mydata$int, "month")
mydata$OverallTime <- round(mydata$OverallTime, digits = 1)

{r}
## Recoding mydata$Outcome into mydata$Outcome2
mydata$Outcome2 <- recode(mydata$Outcome,
               "Alive" = "0",
               "Dead" = "1")
mydata$Outcome2 <- as.numeric(mydata$Outcome2)

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20.2 Kaplan-Meier

{r Kaplan-Meier}
mydata %>%
  finalfit::surv_plot(dependent,
                      explanatory,
                      xlab='Time (months)',
                      pval=TRUE,
                      legend = 'none',
                      break.time.by = 12,
                      xlim = c(0,60),
                      legend.labs = c('a','b')
)

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20.3 Sağkalım Tabloları

{r}
km_fit <- survfit(dependent ~ explanatory,
                  data = mydata)
km_fit

{r, eval=FALSE, include=FALSE}
library(survival)
km <- with(mydata, Surv(OverallTime, Outcome2))
# head(km,80)
# plot(km)

{r 1-3-5-yr}
summary(km_fit, times = c(12,36,60))

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20.4 Pairwise comparison

{r}
survminer::pairwise_survdiff(formula = Surv(time, Outcome) ~ Group, 
                             data = mydata,
                             p.adjust.method = "BH")

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20.5 Multivariate Analysis Survival

{r Multivariate Analysis, eval=FALSE, include=FALSE}
library(finalfit)
library(survival)
explanatoryMultivariate <- explanatoryKM
dependentMultivariate <- dependentKM
mydata %>%
  finalfit(dependentMultivariate, explanatoryMultivariate) -> tMultivariate
knitr::kable(tMultivariate, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))

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21 jamovi

21.1 jamovi ve R entegrasyonu

Rj Editor – Analyse your data with R in jamovi

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21.2 {jmv} paket kodları

jamovi syntax mode

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22 Güncellemeler olunca kodlar çalışacak mı?

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22.1 Paket Kütüphaneleri

• packrat / renv

https://environments.rstudio.com

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22.2 Docker

• docker

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22.2.1 The Rocker Project

Docker Containers for the R Environment

docker run --rm -ti rocker/r-base

Or get started with an RStudio® instance:

docker run -e PASSWORD=yourpassword --rm -p 8787:8787 rocker/rstudio

and point your browser to localhost:8787 Log in with user/password rstudio/yourpassword

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Managing containers

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22.3 Yeni R sürümleri

• RSwitch

https://rud.is/rswitch/

• Using RSwitch

https://rud.is/rswitch/guide/

: scale 30%

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23 Yedeklemeyi nasıl yapacağız

23.1 Projeyi düzgün organize edin

• pdf
• R
• images
• bib

{r load library}
source(file = here::here("R", "loadLibrary.R"))

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23.2 Save Final Data

{r}
saved data after analysis to `mydata.xlsx`.

save.image(file = here::here("data", "mydata_work_space.RData"))

readr::write_rds(x = mydata, path = here::here("data", "mydata_afteranalysis.rds"))

saveRDS(object = mydata, file = here::here("data", "mydata.rds"))

writexl::write_xlsx(mydata, here::here("data", "mydata.xlsx"))

paste0(rownames(file.info(here::here("data", "mydata.xlsx"))), " : ", file.info(here::here("data", "mydata.xlsx"))$ctime)

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23.3 GitHub Yedekleme

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24 Her dökümanın sonuna kullandığınız kütüphaneler için atıf yazdırabilirsiniz

{r}
citation()

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24.1 Libraries Used

citation()

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24.2 Bu oturuma spesifik kullanılan paketler

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24.3 Tek tek paket atıfları

{r library citations}
citation("tidyverse")
citation("readxl")
citation("janitor")
citation("report")
citation("finalfit")
citation("ggstatplot")

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24.4 Jamovi ve R için atıf örneği

• The jamovi project (2019). jamovi. (Version 0.9) [Computer Software]. Retrieved from https://www.jamovi.org.

• R Core Team (2018). R: A Language and envionment for statistical computing. [Computer software]. Retrieved from https://cran.r-project.org/.

• Fox, J., & Weisberg, S. (2018). car: Companion to Applied Regression. [R package]. Retrieved from https://cran.r-project.org/package=car.

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25 Her dökümanın sonuna oturum detaylarınızı yazdırabilirsiniz

{r session info, echo=TRUE}
sessionInfo()

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25.1 Session Info

sessionInfo()

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26 Sonraki Konular

• RStudio ile GitHub kullanımı
• …

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27 Önerilen Kaynaklar

• Reproducible Templates for Analysis and Dissemination

• Happy Git and GitHub for the useR

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28 Sunum Linkleri

https://sbalci.github.io/MyRCodesForDataAnalysis/R-Markdown.nb.html https://sbalci.github.io/MyRCodesForDataAnalysis/R-Markdown.html

https://forms.gle/UqGJBiAjB8uLPRon8

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29 Geri Bildirim

• Geri bildirim için tıklayınız: Geri bildirim formu

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30 İletişim

Completed on 2019-11-01.

Serdar Balci, MD, Pathologist
drserdarbalci@gmail.com

https://rpubs.com/sbalci/CV
https://sbalci.github.io/
https://github.com/sbalci
https://twitter.com/serdarbalci